Instructions to use Rexhaif/Qwen3-4B-MTEval-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Rexhaif/Qwen3-4B-MTEval-SFT with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Rexhaif/Qwen3-4B-MTEval-SFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Rexhaif/Qwen3-4B-MTEval-SFT")
model = AutoModelForCausalLM.from_pretrained("Rexhaif/Qwen3-4B-MTEval-SFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Rexhaif/Qwen3-4B-MTEval-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Rexhaif/Qwen3-4B-MTEval-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Rexhaif/Qwen3-4B-MTEval-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Rexhaif/Qwen3-4B-MTEval-SFT

SGLang

How to use Rexhaif/Qwen3-4B-MTEval-SFT with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Rexhaif/Qwen3-4B-MTEval-SFT" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Rexhaif/Qwen3-4B-MTEval-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Rexhaif/Qwen3-4B-MTEval-SFT" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Rexhaif/Qwen3-4B-MTEval-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Rexhaif/Qwen3-4B-MTEval-SFT with Docker Model Runner:
```
docker model run hf.co/Rexhaif/Qwen3-4B-MTEval-SFT
```

See axolotl config

axolotl version: 0.9.2

base_model: Qwen/Qwen3-4B
# Automatically upload checkpoint and final model to HF
hub_model_id: Rexhaif/Qwen3-4B-MTEval-SFT
hub_private_repo: false


load_in_8bit: false
load_in_4bit: false
strict: false

chat_template: tokenizer_default
datasets:
  - path: Rexhaif/wmt23-pairs-sft
    split: "train"
    type: chat_template
    field_messages: messages
    roles_to_train: ["assistant"]

shuffle_merged_datasets: true

skip_prepare_dataset: false
dataset_prepared_path: ./data/wmt23-pairs-sft
output_dir: /hnvme/workspace/v106be28-outputs/sft-4b

dataloader_prefetch_factor: 32
dataloader_num_workers: 2
dataloader_pin_memory: true

gc_steps: 1

sequence_len: 512
sample_packing: false
eval_sample_packing: false
pad_to_sequence_len: false

wandb_project: llm-reasoning-mt-eval
wandb_entity:
wandb_name: qwen3-4b-sft

plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
gradient_accumulation_steps: 2
micro_batch_size: 32  # should match num_generations / num_gpus

optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 5.0e-5
cosine_min_lr_ratio: 1.0e-7
max_grad_norm: 1.0
weight_decay: 0.1

bf16: true
tf32: true

flash_attention: true
flash_attn_fuse_qkv: true
flash_attn_fuse_mlp: true
auto_resume_from_checkpoints: true

n_epochs: 3
logging_steps: 10
warmup_ratio: 0.1
evals_per_epoch: 10
saves_per_epoch: 10
save_total_limit: 1
#max_steps: 5000
seed: 42
val_set_size: 0.01

gradient_checkpointing: false
gradient_checkpointing_kwargs:
  use_reentrant: false

Qwen3-4B-MTEval-SFT

This model is a fine-tuned version of Qwen/Qwen3-4B on the Rexhaif/wmt23-pairs-sft dataset. It achieves the following results on the evaluation set:

Loss: 0.0511

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 2
total_train_batch_size: 256
total_eval_batch_size: 128
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 101
num_epochs: 1.0

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0010	1	19.7622
0.251	0.1003	102	0.2284
0.2008	0.2007	204	0.1928
0.1571	0.3010	306	0.1638
0.1264	0.4014	408	0.1307
0.0964	0.5017	510	0.1090
0.0933	0.6021	612	0.0939
0.0628	0.7024	714	0.0762
0.0581	0.8028	816	0.0598
0.0519	0.9031	918	0.0511

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.5.1
Tokenizers 0.21.1

Downloads last month: 2

Safetensors

Model size

4B params

Tensor type

BF16

Model tree for Rexhaif/Qwen3-4B-MTEval-SFT

Base model

Qwen/Qwen3-4B-Base

Finetuned

Qwen/Qwen3-4B

Finetuned

(701)

this model

Rexhaif
/

Qwen3-4B-MTEval-SFT

Qwen3-4B-MTEval-SFT

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for Rexhaif/Qwen3-4B-MTEval-SFT

Dataset used to train Rexhaif/Qwen3-4B-MTEval-SFT